* FILE LAST RUN ON 02/06/2024 USING STATA/SE 17.0

clear all
set maxvar 25000
set matsize 11000

cd ""
* Please change the directory according to your own set-up

/* TO RUN THIS FILE
1) Make sure the GSS_final.dta is in a GSS subfolder 
2) Make sure that the do file creating the main tables are in the folder Table
3) Please install programme estout in case at ssc install estout, replace
4) Please install programme coefplot in case at ssc install coefplot, replace
5) Please create a subfolder Results
*/

use .\GSS\GSS_final.dta, clear

*** REGRESSIONS

* OLS

* Happiness 



local sample
local mean
local fe_location
local year

reg Happy WhiteMale if year>=2014, robust
* Regression without controls. As noted in the Supplementary Material, for comparability, I only look at years post 2014
qui sum Happy if e(sample)
estadd local mean =`r(mean)'
estadd local sample "All"
est store happy1
reg Happy WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014, robust
* Regression with controls. As noted in the Supplementary Material, for comparability, I only look at years post 2014
qui sum Happy if e(sample)
estadd local mean =`r(mean)'
estadd local fe_location "$\checkmark$"
estadd local year "$\checkmark$"
estadd local sample "All"
est store happy2

reg Happy WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 i.income16 public childs i.relig i.region i.year if year>=2014, robust
* Regression with income on 16 categories, less observations as year 2014 is absent
* NOT SHOWN IN PAPER

reg Happy WhiteMale  HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year  if  White ==1 & year>=2014, robust
* Regression with controls and restricting sample to Whites. As noted in the Supplementary Material, for comparability, I only look at years post 2014

qui sum Happy if e(sample)
estadd local mean =`r(mean)'
estadd local fe_location "$\checkmark$"
estadd local year "$\checkmark$"
estadd local sample "White"
est store happy3



qui do .\Table\Table_Happy_GSS
* The following command exports the table for Online Appendix D (same for every regression below)

* The following command exports the full table for Online Appendix F (available in the file Angry White Males - Dataverse.pdf in the APSR dataverse for this article)
esttab   happy2 happy3  using "./Results/fulltable_happy_GSS", tex replace se obslast collabels(, none) nomtitle /// 
	/// We export the estimates from column1 to column4 in the file ab_comparison (in a specific folder)
	/// in the form of a tex file (you can choose other format) displaying standard errors instead of t-stat, with observations last
	 star(* 0.10 ** 0.05 *** 0.01) wrap mlabel("Self-rated happiness" "Self-rated happiness" ) ///
	/// Add the stars and label the columns
	 nonumbers cells(b(star fmt(4)) se(fmt(4) par)) compress label keep(WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus* Income public childs *.relig )  gaps  ///
	 /// The most important commands here are that we ask for 3 decimals (fmt(3)) for coeff and st. errors, we also use label
	 stats(sample fe_location year N, fmt(a1 %9.0fc 0)  labels(`"Sample"' `"Location FE"' `"Year FE"' `"N.obs"'))  legend
	 /// Here we have the stats shown after the coefficients as well as their associated label

	 
* HAPPINESS over time	 
forvalue j=1(1)9 {
	* The following loop looks at the WhiteMale coefficient over time in regression wiht controls, separating periods in 5-year intervals
qui	reg Happy WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year >= 1972+(`j'-1)*5 & year<1972 + `j'*5 , robust
estadd local fe_location "$\checkmark$"
estadd local year "$\checkmark$"
estadd local sample "All"

est store year`j'
}

qui	reg Happy WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year >= 2018 , robust
* The regression with controls recover the coefficient on white male for the years post-2018
estadd local fe_location "$\checkmark$"
estadd local year "$\checkmark$"
estadd local sample "All"
est store year10



* The figure below displays the over time coefficients in a plot.
coefplot (year1, asequation(1972-76)  \ year2, asequation(1977-81)  \ year3, asequation(1982-86) \ year4, asequation(1987-91) \ year5, asequation(1992-96) \ year6, asequation(1997-01) \ year7, asequation(2002-06) \ year8, asequation(2007-2011) \ year9, asequation(2012-16) \ year10, asequation(2017-2022)), ///
 keep(WhiteMale) yline(0)  vertical plotregion(fcolor(white)) graphregion(fcolor(white))   swap xtitle("Coefficient for White Male")  xlabel(,labsize(vsmall))
 gr export "./Results/HappinessOverTime.pdf", as(pdf) replace 

* The following command exports the full table for Online Appendix F (available in the file Angry White Males - Dataverse.pdf in the APSR dataverse for this article)
esttab   year1 year2 year3 year4 year5  using "./Results/fulltable_happy_time1", tex replace se obslast collabels(, none) nomtitle /// 
	/// We export the estimates from column1 to column4 in the file ab_comparison (in a specific folder)
	/// in the form of a tex file (you can choose other format) displaying standard errors instead of t-stat, with observations last
	 star(* 0.10 ** 0.05 *** 0.01) wrap mlabel("1972-76" "1977-81" "1982-86" "1987-91" "1992-1996") ///
	/// Add the stars and label the columns
	 nonumbers cells(b(star fmt(4)) se(fmt(4) par)) compress label keep(WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus* Income public childs *.relig )  gaps  ///
	 /// The most important commands here are that we ask for 3 decimals (fmt(3)) for coeff and st. errors, we also use label
	 stats(sample fe_location year N, fmt(a1 %9.0fc 0)  labels(`"Sample"' `"Location FE"' `"Year FE"' `"N.obs"'))  legend
	 /// Here we have the stats shown after the coefficients as well as their associated label

* To make the table easier to read, I split the table into two tables.
esttab   year6 year7 year8 year9 year10  using "./Results/fulltable_happy_time2", tex replace se obslast collabels(, none) nomtitle /// 
	/// We export the estimates from column1 to column4 in the file ab_comparison (in a specific folder)
	/// in the form of a tex file (you can choose other format) displaying standard errors instead of t-stat, with observations last
	 star(* 0.10 ** 0.05 *** 0.01) wrap mlabel("1997-01" "2002-06" "2007-11" "2012-16" "2017-22") ///
	/// Add the stars and label the columns
	 nonumbers cells(b(star fmt(4)) se(fmt(4) par)) compress label keep(WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus* Income public childs *.relig )  gaps  ///
	 /// The most important commands here are that we ask for 3 decimals (fmt(3)) for coeff and st. errors, we also use label
	 stats(sample fe_location year N, fmt(a1 %9.0fc 0)  labels(`"Sample"' `"Location FE"' `"Year FE"' `"N.obs"'))  legend
	 /// Here we have the stats shown after the coefficients as well as their associated label

 
 
***************************** TOO MUCH OF AFF ACTION **************************	 



* Discrimination regression all observations
	
* For each policy/item (affirmative action, feeling that Blacks can progress without favors, spending for Blacks), regressions are run with and without control

	
local fe_year
local fe_location


reg oppaffact WhiteMale if year>=2014, robust
qui sum oppaffact if e(sample)
estadd local mean= `r(mean)'
est store discrimmodelQ1

reg oppaffact WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014, robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
qui sum oppaffact if e(sample)
estadd local mean= `r(mean)'
est store discrimmodelQ2

reg blackeffort WhiteMale if year>=2014, robust
estadd local fe_year "$\checkmark$"
qui sum blackeffort if e(sample)
estadd local mean= `r(mean)'
est store discrimmodelQ3

reg blackeffort WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014, robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
qui sum blackeffort if e(sample)
estadd local mean= `r(mean)'
est store discrimmodelQ4

reg blackTooMuch1 WhiteMale if year>=2014, robust
qui sum blackTooMuch1 if e(sample)
estadd local mean= `r(mean)'
est store discrimmodelQ5

reg blackTooMuch1 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014, robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
qui sum blackTooMuch1 if e(sample)
estadd local mean= `r(mean)'
est store discrimmodelQ6

reg blackTooMuch1 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 i.income16 public childs i.relig i.region i.year if year>=2014, robust
* Regression with income on 16 categories, less observations as year 2014 is absent
* NOT SHOWN IN PAPER


reg blackTooMuch2 WhiteMale if year>=2014 , robust
qui sum blackTooMuch2 if e(sample)
estadd local mean= `r(mean)'
est store discrimmodelQ7


reg blackTooMuch2 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014, robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
qui sum blackTooMuch2 if e(sample)
estadd local mean= `r(mean)'
est store discrimmodelQ8

reg blackTooMuch2 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 i.income16 public childs i.relig i.region i.year if year>=2014, robust
* Regression with income on 16 categories, less observations as year 2014 is absent
* NOT SHOWN IN PAPER


qui do .\Table\Table_Discrim_GSS 
* The following command exports the table for Online Appendix D (same for every regression below)

* The following command exports the full table for Online Appendix F (available in the file Angry White Males - Dataverse.pdf in the APSR dataverse for this article)
esttab  discrimmodelQ2 discrimmodelQ4 discrimmodelQ6 discrimmodelQ8  using "./Results/fulltable_discrim_GSS", tex replace se obslast collabels(, none) nomtitle /// 
	/// We export the estimates from column1 to column4 in the file ab_comparison (in a specific folder)
	/// in the form of a tex file (you can choose other format) displaying standard errors instead of t-stat, with observations last
	 star(* 0.10 ** 0.05 *** 0.01) wrap mlabel("Opposed aff action" "Resentment" "Too much assistance" "Too much for improv." ) ///
	/// Add the stars and label the columns
	 nonumbers cells(b(star fmt(4)) se(fmt(4) par)) compress label keep(WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus* Income public childs *.relig )  gaps  ///
	 /// The most important commands here are that we ask for 3 decimals (fmt(3)) for coeff and st. errors, we also use label
	 stats(fe_location fe_year N, fmt(a1 %9.0fc 0)  labels(`"Location FE"' `"Year FE"' `"N.obs"'))  legend
	 /// Here we have the stats shown after the coefficients as well as their associated label
	 



* Discrimination regression by education

* For the two spending items (on improvement and on assistance), three regressions with controls are run: 
* The first for individuals who have not finished High School or not revealed their degrees
* The second for individuals who have finished High School and completed none of some university education
* The third for individuals who have at least a Bachelor degree


local fe_year
local educ
local fe_location


reg blackTooMuch1 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if  HighSchool == 0 & HigherEdu == 0 & year>=2014, robust
qui sum blackTooMuch1 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local educ "No qualif./answer"
est store discrim_educ1

reg blackTooMuch1 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year  if  HighSchool == 1 & HigherEdu == 0 & year>=2014, robust
qui sum blackTooMuch1 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local educ "High School"
est store discrim_educ2

reg blackTooMuch1 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year  if  HighSchool == 0 & HigherEdu == 1 & year>=2014, robust
qui sum blackTooMuch1 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local educ "University"
est store discrim_educ3

reg blackTooMuch2 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year  if  HighSchool == 0 & HigherEdu == 0 & year>=2014, robust
qui sum blackTooMuch2 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_location "$\checkmark$"
estadd local fe_year "$\checkmark$"
estadd local educ "No qualif./answer"
est store discrim_educ4

reg blackTooMuch2 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if  HighSchool == 1 & HigherEdu == 0 & year>=2014, robust
qui sum blackTooMuch2 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local educ "High School"
est store discrim_educ5

reg blackTooMuch2 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year  if  HighSchool == 0 & HigherEdu == 1 & year>=2014, robust
qui sum blackTooMuch2 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local educ "University"
est store discrim_educ6

qui do .\Table\Table_Assistance_educ 
* The following command exports the table for Online Appendix D (same for every regression below)

* The following command exports the full table for Online Appendix F (available in the file Angry White Males - Dataverse.pdf in the APSR dataverse for this article)
esttab  discrim_educ* using "./Results/fulltable_assistance_GSS", tex replace se obslast collabels(, none) nomtitle /// 
	/// We export the estimates from column1 to column4 in the file ab_comparison (in a specific folder)
	/// in the form of a tex file (you can choose other format) displaying standard errors instead of t-stat, with observations last
	 star(* 0.10 ** 0.05 *** 0.01) wrap mlabel("Too much assistance" "Too much assistance" "Too much assistance" "Too much for improv." "Too much for improv." "Too much for improv." ) ///
	/// Add the stars and label the columns
	 nonumbers cells(b(star fmt(4)) se(fmt(4) par)) compress label keep(WhiteMale Own Married Divorced age  workstatus* Income public childs *.relig )  gaps  ///
	 /// The most important commands here are that we ask for 3 decimals (fmt(3)) for coeff and st. errors, we also use label
	 stats(educ fe_location fe_year N, fmt(a1 %9.0fc 0)  labels(`"Sample"' `"Location FE"' `"Year FE"' `"N.obs"'))  legend
	 /// Here we have the stats shown after the coefficients as well as their associated label
	 
	 


* Discrimination regression by age

* For the two spending items (on improvement and on assistance), three regressions with controls are run: 
* The first for individuals below the age of 25
* The second for individuals between 26 and 64 years old
* The third for individuals over 65 years old



gen age25 = 0 if age!=.
replace age25 = 1 if age <=25
gen age26_64 = 0 if age!=.
replace age26_64 = 1 if age> 25 & age<65
gen age65 = 0 if age!=.
replace age65 = 1 if age >=65 & age!=.

local fe_year
local age
local fe_location


reg blackTooMuch1 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if age25==1 & year>=2014, robust
qui sum blackTooMuch1 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local age "Under 25"
est store discrim_age1

reg blackTooMuch1 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if age26_64==1 & year>=2014, robust
qui sum blackTooMuch1 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local age "26-64"
est store discrim_age2

reg blackTooMuch1 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if age65==1 & year>=2014, robust
qui sum blackTooMuch1 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local age "Over 65"
est store discrim_age3

reg blackTooMuch2 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if age25==1 & year>=2014, robust
qui sum blackTooMuch2 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local age "Under 25"
estadd local fe_location "$\checkmark$"
est store discrim_age4

reg blackTooMuch2 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if age26_64==1 & year>=2014, robust
qui sum blackTooMuch2 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local age "26-64"
est store discrim_age5

reg blackTooMuch2 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if age65==1 & year>=2014, robust
qui sum blackTooMuch2 if e(sample)
estadd local mean= `r(mean)'
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local age "Over 65"
est store discrim_age6

qui do .\Table\Table_Assistance_age 
* The following command exports the table for Online Appendix D (same for every regression below)

* The following command exports the full table for Online Appendix F (available in the file Angry White Males - Dataverse.pdf in the APSR dataverse for this article)
esttab  discrim_age* using "./Results/fulltable_assistance_age_GSS", tex replace se obslast collabels(, none) nomtitle /// 
	/// We export the estimates from column1 to column4 in the file ab_comparison (in a specific folder)
	/// in the form of a tex file (you can choose other format) displaying standard errors instead of t-stat, with observations last
	 star(* 0.10 ** 0.05 *** 0.01) wrap mlabel("Too much assistance" "Too much assistance" "Too much assistance" "Too much for improv." "Too much for improv." "Too much for improv." ) ///
	/// Add the stars and label the columns
	 nonumbers cells(b(star fmt(4)) se(fmt(4) par)) compress label keep(WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus* Income public childs *.relig )  gaps  ///
	 /// The most important commands here are that we ask for 3 decimals (fmt(3)) for coeff and st. errors, we also use label
	 stats(age fe_location fe_year N, fmt(a1 %9.0fc 0)  labels(`"Sample"' `"Location FE"' `"Year FE"' `"N.obs"'))  legend
	 /// Here we have the stats shown after the coefficients as well as their associated label
	 	 
	 
	 
***************************** PROBIT MODELS *************************************
* Here I rerun the main models (without splitting samples by educations or ages), using Probit instead of OLS.

* Happiness

local sample
local fe_location

oprobit Happy WhiteMale, robust
qui sum Happy if e(sample)
estadd local sample "All"
est store happy1
oprobit Happy WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014 , robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local sample "All"
est store happy2
oprobit Happy WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014 & White ==1, robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
estadd local sample "White"
est store happy3




* The following command exports the full table for Online Appendix F (available in the file Angry White Males - Dataverse.pdf in the APSR dataverse for this article)
esttab   happy* using "./Results/fulltable_happy_GSS_oprobit", tex replace se obslast collabels(, none) nomtitle /// 
	/// We export the estimates from column1 to column4 in the file ab_comparison (in a specific folder)
	/// in the form of a tex file (you can choose other format) displaying standard errors instead of t-stat, with observations last
	 star(* 0.10 ** 0.05 *** 0.01) wrap mlabel("Self-rated happiness" "Self-rated happiness" "Self-rated happiness" ) ///
	/// Add the stars and label the columns
	 nonumbers cells(b(star fmt(4)) se(fmt(4) par)) compress label keep(WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus* Income public childs *.relig )  gaps  ///
	 /// The most important commands here are that we ask for 3 decimals (fmt(3)) for coeff and st. errors, we also use label
	 stats(sample fe_location fe_year N, fmt(a1 %9.0fc 0)  labels(`"Sample"' `"Location FE"' `"Year FE"' `"N.obs"'))  legend
	 /// Here we have the stats shown after the coefficients as well as their associated label

* Discrimination	 
	 
local fe_year
local fe_location


probit oppaffact WhiteMale  if year>=2014 , robust
qui sum oppaffact if e(sample)
est store discrimmodelQ1

probit oppaffact WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014, robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
qui sum oppaffact if e(sample)
est store discrimmodelQ2

probit blackeffort WhiteMale  if year>=2014, robust
estadd local fe_year "$\checkmark$"
qui sum blackeffort if e(sample)
est store discrimmodelQ3

probit blackeffort WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year  if year>=2014, robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
qui sum blackeffort if e(sample)
est store discrimmodelQ4

probit blackTooMuch1 WhiteMale  if year>=2014, robust
qui sum blackTooMuch1 if e(sample)
est store discrimmodelQ5

probit blackTooMuch1 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014, robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
qui sum blackTooMuch1 if e(sample)
est store discrimmodelQ6

probit blackTooMuch2 WhiteMale  if year>=2014, robust
qui sum blackTooMuch2 if e(sample)
est store discrimmodelQ7


probit blackTooMuch2 WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus1-workstatus7 Income public childs i.relig i.region i.year if year>=2014, robust
estadd local fe_year "$\checkmark$"
estadd local fe_location "$\checkmark$"
qui sum blackTooMuch2 if e(sample)
est store discrimmodelQ8

* The following command exports the full table for Online Appendix F (available in the file Angry White Males - Dataverse.pdf in the APSR dataverse for this article)
esttab  discrimmodelQ*  using "./Results/fulltable_discrim_probit_GSS", tex replace se obslast collabels(, none) nomtitle /// 
	/// We export the estimates from column1 to column4 in the file ab_comparison (in a specific folder)
	/// in the form of a tex file (you can choose other format) displaying standard errors instead of t-stat, with observations last
	 star(* 0.10 ** 0.05 *** 0.01) wrap mlabel("Opposed aff action" "Opposed aff action" "Resentment" "Resentment" "Too much assistance" "Too much assistance" "Too much for improv."  "Too much for improv." ) ///
	/// Add the stars and label the columns
	 nonumbers cells(b(star fmt(4)) se(fmt(4) par)) compress label keep(WhiteMale HigherEdu HighSchool Own Married Divorced age  workstatus* Income public childs *.relig )  gaps  ///
	 /// The most important commands here are that we ask for 3 decimals (fmt(3)) for coeff and st. errors, we also use label
	 stats(sample fe_location fe_year N, fmt(a1 %9.0fc 0)  labels(`"Sample"' `"Location FE"' `"Year FE"' `"N.obs"'))  legend
	 /// Here we have the stats shown after the coefficients as well as their associated label
	 

